How to Develop Artificial Intelligence Applications

  • Angel Alberich-BayarriEmail author
  • Ana Jiménez Pastor
  • Rafael López González
  • Fabio García Castro


If there is a field where AI is introducing disruptive innovations, it is healthcare, where doctors have to handle a large set of information in every clinical episode. AI developments have demonstrated to be highly specific, being useful to solve repetitive and rule-driven problems without clinical context with human-like performance, and must be understood more as a complement than a substitute of the radiologist. The quantity and heterogeneity of information to be evaluated by radiologists’ mind during the image interpretation process are high. Radiology is not only about image recognition but a high amount of contextual information. In this chapter, the resources needed to implement a successful AI solution in radiology are detailed. All this knowledge must be embraced by the radiological community in order to obtain efficient applications that allow to improve their work, not considering the technology as a threat but as the main driver of opportunities for the future specialists.


Artificial intelligence Imaging biomarkers Structured report Convolutional neural network Data science High-performance computing Graphics processing unit Data annotation Training Testing 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Angel Alberich-Bayarri
    • 1
    • 2
    Email author
  • Ana Jiménez Pastor
    • 2
  • Rafael López González
    • 2
  • Fabio García Castro
    • 2
  1. 1.Biomedical Imaging Research Group (GIBI2^30)La Fe Health Research InstituteValenciaSpain
  2. 2.Quantitative Imaging Biomarkers in Medicine (QUIBIM S.L.)ValenciaSpain

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